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An Exploratory Analysis of COVID Bot vs Human Disinformation Dissemination stemming from the Disinformation Dozen on Telegram (2402.14203v1)

Published 22 Feb 2024 in cs.CY and cs.SI

Abstract: The COVID-19 pandemic of 2021 led to a worldwide health crisis that was accompanied by an infodemic. A group of 12 social media personalities, dubbed the ``Disinformation Dozen", were identified as key in spreading disinformation regarding the COVID-19 virus, treatments, and vaccines. This study focuses on the spread of disinformation propagated by this group on Telegram, a mobile messaging and social media platform. After segregating users into three groups -- the Disinformation Dozen, bots, and humans --, we perform an investigation with a dataset of Telegram messages from January to June 2023, comparatively analyzing temporal, topical, and network features. We observe that the Disinformation Dozen are highly involved in the initial dissemination of disinformation but are not the main drivers of the propagation of disinformation. Bot users are extremely active in conversation threads, while human users are active propagators of information, disseminating posts between Telegram channels through the forwarding mechanism.

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References (56)
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Journal of Computational Social Science 5(1), 949–968 (2022) Walther and McCoy [2021] Walther, S., McCoy, A.: Us extremism on telegram. Perspectives on Terrorism 15(2), 100–124 (2021) Khaund et al. [2020] Khaund, T., Hussain, M.N., Shaik, M., Agarwal, N.: Telegram: Data collection, opportunities and challenges. In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. 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IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer CCDH: The Disinformation Dozen — Center for Countering Digital Hate — CCDH — counterhate.com. https://counterhate.com/research/the-disinformation-dozen/. [Accessed 25-10-2023] (2021) Nogara et al. [2022] Nogara, G., Vishnuprasad, P.S., Cardoso, F., Ayoub, O., Giordano, S., Luceri, L.: The disinformation dozen: An exploratory analysis of covid-19 disinformation proliferation on twitter. In: Proceedings of the 14th ACM Web Science Conference 2022, pp. 348–358 (2022) Krishnan et al. [2021] Krishnan, N., Gu, J., Tromble, R., Abroms, L.C.: Research note: Examining how various social media platforms have responded to covid-19 misinformation. Harvard Kennedy School Misinformation Review 2(6), 1–25 (2021) Forbes [2023] Forbes: Pavel Durov — forbes.com. https://www.forbes.com/profile/pavel-durov/?sh=77a6811e14c5. [Accessed 26-10-2023] (2023) Ng and Loke [2020] Ng, L.H.X., Loke, J.Y.: Analyzing public opinion and misinformation in a covid-19 telegram group chat. IEEE Internet Computing 25(2), 84–91 (2020) Willaert et al. [2022] Willaert, T., Peeters, S., Seijbel, J., Van Raemdonck, N.: Disinformation networks: a quali-quantitative investigation of antagonistic dutch-speaking telegram channels. First Monday (2022) Sosa and Sharoff [2022] Sosa, J., Sharoff, S.: Multimodal pipeline for collection of misinformation data from telegram. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 1480–1489 (2022) Al-Rawi [2022] Al-Rawi, A.: News loopholing: Telegram news as portable alternative media. Journal of Computational Social Science 5(1), 949–968 (2022) Walther and McCoy [2021] Walther, S., McCoy, A.: Us extremism on telegram. Perspectives on Terrorism 15(2), 100–124 (2021) Khaund et al. [2020] Khaund, T., Hussain, M.N., Shaik, M., Agarwal, N.: Telegram: Data collection, opportunities and challenges. In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Nogara, G., Vishnuprasad, P.S., Cardoso, F., Ayoub, O., Giordano, S., Luceri, L.: The disinformation dozen: An exploratory analysis of covid-19 disinformation proliferation on twitter. In: Proceedings of the 14th ACM Web Science Conference 2022, pp. 348–358 (2022) Krishnan et al. [2021] Krishnan, N., Gu, J., Tromble, R., Abroms, L.C.: Research note: Examining how various social media platforms have responded to covid-19 misinformation. Harvard Kennedy School Misinformation Review 2(6), 1–25 (2021) Forbes [2023] Forbes: Pavel Durov — forbes.com. https://www.forbes.com/profile/pavel-durov/?sh=77a6811e14c5. [Accessed 26-10-2023] (2023) Ng and Loke [2020] Ng, L.H.X., Loke, J.Y.: Analyzing public opinion and misinformation in a covid-19 telegram group chat. IEEE Internet Computing 25(2), 84–91 (2020) Willaert et al. [2022] Willaert, T., Peeters, S., Seijbel, J., Van Raemdonck, N.: Disinformation networks: a quali-quantitative investigation of antagonistic dutch-speaking telegram channels. First Monday (2022) Sosa and Sharoff [2022] Sosa, J., Sharoff, S.: Multimodal pipeline for collection of misinformation data from telegram. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 1480–1489 (2022) Al-Rawi [2022] Al-Rawi, A.: News loopholing: Telegram news as portable alternative media. Journal of Computational Social Science 5(1), 949–968 (2022) Walther and McCoy [2021] Walther, S., McCoy, A.: Us extremism on telegram. Perspectives on Terrorism 15(2), 100–124 (2021) Khaund et al. [2020] Khaund, T., Hussain, M.N., Shaik, M., Agarwal, N.: Telegram: Data collection, opportunities and challenges. In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Krishnan, N., Gu, J., Tromble, R., Abroms, L.C.: Research note: Examining how various social media platforms have responded to covid-19 misinformation. Harvard Kennedy School Misinformation Review 2(6), 1–25 (2021) Forbes [2023] Forbes: Pavel Durov — forbes.com. https://www.forbes.com/profile/pavel-durov/?sh=77a6811e14c5. [Accessed 26-10-2023] (2023) Ng and Loke [2020] Ng, L.H.X., Loke, J.Y.: Analyzing public opinion and misinformation in a covid-19 telegram group chat. IEEE Internet Computing 25(2), 84–91 (2020) Willaert et al. [2022] Willaert, T., Peeters, S., Seijbel, J., Van Raemdonck, N.: Disinformation networks: a quali-quantitative investigation of antagonistic dutch-speaking telegram channels. First Monday (2022) Sosa and Sharoff [2022] Sosa, J., Sharoff, S.: Multimodal pipeline for collection of misinformation data from telegram. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 1480–1489 (2022) Al-Rawi [2022] Al-Rawi, A.: News loopholing: Telegram news as portable alternative media. Journal of Computational Social Science 5(1), 949–968 (2022) Walther and McCoy [2021] Walther, S., McCoy, A.: Us extremism on telegram. Perspectives on Terrorism 15(2), 100–124 (2021) Khaund et al. [2020] Khaund, T., Hussain, M.N., Shaik, M., Agarwal, N.: Telegram: Data collection, opportunities and challenges. In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Forbes: Pavel Durov — forbes.com. https://www.forbes.com/profile/pavel-durov/?sh=77a6811e14c5. [Accessed 26-10-2023] (2023) Ng and Loke [2020] Ng, L.H.X., Loke, J.Y.: Analyzing public opinion and misinformation in a covid-19 telegram group chat. IEEE Internet Computing 25(2), 84–91 (2020) Willaert et al. [2022] Willaert, T., Peeters, S., Seijbel, J., Van Raemdonck, N.: Disinformation networks: a quali-quantitative investigation of antagonistic dutch-speaking telegram channels. First Monday (2022) Sosa and Sharoff [2022] Sosa, J., Sharoff, S.: Multimodal pipeline for collection of misinformation data from telegram. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 1480–1489 (2022) Al-Rawi [2022] Al-Rawi, A.: News loopholing: Telegram news as portable alternative media. Journal of Computational Social Science 5(1), 949–968 (2022) Walther and McCoy [2021] Walther, S., McCoy, A.: Us extremism on telegram. Perspectives on Terrorism 15(2), 100–124 (2021) Khaund et al. [2020] Khaund, T., Hussain, M.N., Shaik, M., Agarwal, N.: Telegram: Data collection, opportunities and challenges. In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Loke, J.Y.: Analyzing public opinion and misinformation in a covid-19 telegram group chat. IEEE Internet Computing 25(2), 84–91 (2020) Willaert et al. [2022] Willaert, T., Peeters, S., Seijbel, J., Van Raemdonck, N.: Disinformation networks: a quali-quantitative investigation of antagonistic dutch-speaking telegram channels. First Monday (2022) Sosa and Sharoff [2022] Sosa, J., Sharoff, S.: Multimodal pipeline for collection of misinformation data from telegram. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 1480–1489 (2022) Al-Rawi [2022] Al-Rawi, A.: News loopholing: Telegram news as portable alternative media. Journal of Computational Social Science 5(1), 949–968 (2022) Walther and McCoy [2021] Walther, S., McCoy, A.: Us extremism on telegram. Perspectives on Terrorism 15(2), 100–124 (2021) Khaund et al. [2020] Khaund, T., Hussain, M.N., Shaik, M., Agarwal, N.: Telegram: Data collection, opportunities and challenges. In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Willaert, T., Peeters, S., Seijbel, J., Van Raemdonck, N.: Disinformation networks: a quali-quantitative investigation of antagonistic dutch-speaking telegram channels. First Monday (2022) Sosa and Sharoff [2022] Sosa, J., Sharoff, S.: Multimodal pipeline for collection of misinformation data from telegram. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 1480–1489 (2022) Al-Rawi [2022] Al-Rawi, A.: News loopholing: Telegram news as portable alternative media. Journal of Computational Social Science 5(1), 949–968 (2022) Walther and McCoy [2021] Walther, S., McCoy, A.: Us extremism on telegram. Perspectives on Terrorism 15(2), 100–124 (2021) Khaund et al. [2020] Khaund, T., Hussain, M.N., Shaik, M., Agarwal, N.: Telegram: Data collection, opportunities and challenges. In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Sosa, J., Sharoff, S.: Multimodal pipeline for collection of misinformation data from telegram. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 1480–1489 (2022) Al-Rawi [2022] Al-Rawi, A.: News loopholing: Telegram news as portable alternative media. Journal of Computational Social Science 5(1), 949–968 (2022) Walther and McCoy [2021] Walther, S., McCoy, A.: Us extremism on telegram. Perspectives on Terrorism 15(2), 100–124 (2021) Khaund et al. [2020] Khaund, T., Hussain, M.N., Shaik, M., Agarwal, N.: Telegram: Data collection, opportunities and challenges. In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Al-Rawi, A.: News loopholing: Telegram news as portable alternative media. Journal of Computational Social Science 5(1), 949–968 (2022) Walther and McCoy [2021] Walther, S., McCoy, A.: Us extremism on telegram. Perspectives on Terrorism 15(2), 100–124 (2021) Khaund et al. [2020] Khaund, T., Hussain, M.N., Shaik, M., Agarwal, N.: Telegram: Data collection, opportunities and challenges. In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Walther, S., McCoy, A.: Us extremism on telegram. Perspectives on Terrorism 15(2), 100–124 (2021) Khaund et al. [2020] Khaund, T., Hussain, M.N., Shaik, M., Agarwal, N.: Telegram: Data collection, opportunities and challenges. In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Khaund, T., Hussain, M.N., Shaik, M., Agarwal, N.: Telegram: Data collection, opportunities and challenges. In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. 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[2021] Krishnan, N., Gu, J., Tromble, R., Abroms, L.C.: Research note: Examining how various social media platforms have responded to covid-19 misinformation. Harvard Kennedy School Misinformation Review 2(6), 1–25 (2021) Forbes [2023] Forbes: Pavel Durov — forbes.com. https://www.forbes.com/profile/pavel-durov/?sh=77a6811e14c5. [Accessed 26-10-2023] (2023) Ng and Loke [2020] Ng, L.H.X., Loke, J.Y.: Analyzing public opinion and misinformation in a covid-19 telegram group chat. IEEE Internet Computing 25(2), 84–91 (2020) Willaert et al. [2022] Willaert, T., Peeters, S., Seijbel, J., Van Raemdonck, N.: Disinformation networks: a quali-quantitative investigation of antagonistic dutch-speaking telegram channels. First Monday (2022) Sosa and Sharoff [2022] Sosa, J., Sharoff, S.: Multimodal pipeline for collection of misinformation data from telegram. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 1480–1489 (2022) Al-Rawi [2022] Al-Rawi, A.: News loopholing: Telegram news as portable alternative media. Journal of Computational Social Science 5(1), 949–968 (2022) Walther and McCoy [2021] Walther, S., McCoy, A.: Us extremism on telegram. Perspectives on Terrorism 15(2), 100–124 (2021) Khaund et al. [2020] Khaund, T., Hussain, M.N., Shaik, M., Agarwal, N.: Telegram: Data collection, opportunities and challenges. In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Krishnan, N., Gu, J., Tromble, R., Abroms, L.C.: Research note: Examining how various social media platforms have responded to covid-19 misinformation. Harvard Kennedy School Misinformation Review 2(6), 1–25 (2021) Forbes [2023] Forbes: Pavel Durov — forbes.com. https://www.forbes.com/profile/pavel-durov/?sh=77a6811e14c5. [Accessed 26-10-2023] (2023) Ng and Loke [2020] Ng, L.H.X., Loke, J.Y.: Analyzing public opinion and misinformation in a covid-19 telegram group chat. IEEE Internet Computing 25(2), 84–91 (2020) Willaert et al. [2022] Willaert, T., Peeters, S., Seijbel, J., Van Raemdonck, N.: Disinformation networks: a quali-quantitative investigation of antagonistic dutch-speaking telegram channels. First Monday (2022) Sosa and Sharoff [2022] Sosa, J., Sharoff, S.: Multimodal pipeline for collection of misinformation data from telegram. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 1480–1489 (2022) Al-Rawi [2022] Al-Rawi, A.: News loopholing: Telegram news as portable alternative media. Journal of Computational Social Science 5(1), 949–968 (2022) Walther and McCoy [2021] Walther, S., McCoy, A.: Us extremism on telegram. Perspectives on Terrorism 15(2), 100–124 (2021) Khaund et al. [2020] Khaund, T., Hussain, M.N., Shaik, M., Agarwal, N.: Telegram: Data collection, opportunities and challenges. In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Forbes: Pavel Durov — forbes.com. https://www.forbes.com/profile/pavel-durov/?sh=77a6811e14c5. [Accessed 26-10-2023] (2023) Ng and Loke [2020] Ng, L.H.X., Loke, J.Y.: Analyzing public opinion and misinformation in a covid-19 telegram group chat. IEEE Internet Computing 25(2), 84–91 (2020) Willaert et al. [2022] Willaert, T., Peeters, S., Seijbel, J., Van Raemdonck, N.: Disinformation networks: a quali-quantitative investigation of antagonistic dutch-speaking telegram channels. First Monday (2022) Sosa and Sharoff [2022] Sosa, J., Sharoff, S.: Multimodal pipeline for collection of misinformation data from telegram. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 1480–1489 (2022) Al-Rawi [2022] Al-Rawi, A.: News loopholing: Telegram news as portable alternative media. Journal of Computational Social Science 5(1), 949–968 (2022) Walther and McCoy [2021] Walther, S., McCoy, A.: Us extremism on telegram. Perspectives on Terrorism 15(2), 100–124 (2021) Khaund et al. [2020] Khaund, T., Hussain, M.N., Shaik, M., Agarwal, N.: Telegram: Data collection, opportunities and challenges. In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Loke, J.Y.: Analyzing public opinion and misinformation in a covid-19 telegram group chat. IEEE Internet Computing 25(2), 84–91 (2020) Willaert et al. [2022] Willaert, T., Peeters, S., Seijbel, J., Van Raemdonck, N.: Disinformation networks: a quali-quantitative investigation of antagonistic dutch-speaking telegram channels. First Monday (2022) Sosa and Sharoff [2022] Sosa, J., Sharoff, S.: Multimodal pipeline for collection of misinformation data from telegram. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 1480–1489 (2022) Al-Rawi [2022] Al-Rawi, A.: News loopholing: Telegram news as portable alternative media. Journal of Computational Social Science 5(1), 949–968 (2022) Walther and McCoy [2021] Walther, S., McCoy, A.: Us extremism on telegram. Perspectives on Terrorism 15(2), 100–124 (2021) Khaund et al. [2020] Khaund, T., Hussain, M.N., Shaik, M., Agarwal, N.: Telegram: Data collection, opportunities and challenges. In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Willaert, T., Peeters, S., Seijbel, J., Van Raemdonck, N.: Disinformation networks: a quali-quantitative investigation of antagonistic dutch-speaking telegram channels. First Monday (2022) Sosa and Sharoff [2022] Sosa, J., Sharoff, S.: Multimodal pipeline for collection of misinformation data from telegram. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 1480–1489 (2022) Al-Rawi [2022] Al-Rawi, A.: News loopholing: Telegram news as portable alternative media. Journal of Computational Social Science 5(1), 949–968 (2022) Walther and McCoy [2021] Walther, S., McCoy, A.: Us extremism on telegram. Perspectives on Terrorism 15(2), 100–124 (2021) Khaund et al. [2020] Khaund, T., Hussain, M.N., Shaik, M., Agarwal, N.: Telegram: Data collection, opportunities and challenges. In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Sosa, J., Sharoff, S.: Multimodal pipeline for collection of misinformation data from telegram. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 1480–1489 (2022) Al-Rawi [2022] Al-Rawi, A.: News loopholing: Telegram news as portable alternative media. Journal of Computational Social Science 5(1), 949–968 (2022) Walther and McCoy [2021] Walther, S., McCoy, A.: Us extremism on telegram. Perspectives on Terrorism 15(2), 100–124 (2021) Khaund et al. [2020] Khaund, T., Hussain, M.N., Shaik, M., Agarwal, N.: Telegram: Data collection, opportunities and challenges. In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Al-Rawi, A.: News loopholing: Telegram news as portable alternative media. Journal of Computational Social Science 5(1), 949–968 (2022) Walther and McCoy [2021] Walther, S., McCoy, A.: Us extremism on telegram. Perspectives on Terrorism 15(2), 100–124 (2021) Khaund et al. [2020] Khaund, T., Hussain, M.N., Shaik, M., Agarwal, N.: Telegram: Data collection, opportunities and challenges. In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Walther, S., McCoy, A.: Us extremism on telegram. Perspectives on Terrorism 15(2), 100–124 (2021) Khaund et al. [2020] Khaund, T., Hussain, M.N., Shaik, M., Agarwal, N.: Telegram: Data collection, opportunities and challenges. In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Khaund, T., Hussain, M.N., Shaik, M., Agarwal, N.: Telegram: Data collection, opportunities and challenges. In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. 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[Accessed 26-10-2023] (2023) Ng and Loke [2020] Ng, L.H.X., Loke, J.Y.: Analyzing public opinion and misinformation in a covid-19 telegram group chat. IEEE Internet Computing 25(2), 84–91 (2020) Willaert et al. [2022] Willaert, T., Peeters, S., Seijbel, J., Van Raemdonck, N.: Disinformation networks: a quali-quantitative investigation of antagonistic dutch-speaking telegram channels. First Monday (2022) Sosa and Sharoff [2022] Sosa, J., Sharoff, S.: Multimodal pipeline for collection of misinformation data from telegram. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 1480–1489 (2022) Al-Rawi [2022] Al-Rawi, A.: News loopholing: Telegram news as portable alternative media. Journal of Computational Social Science 5(1), 949–968 (2022) Walther and McCoy [2021] Walther, S., McCoy, A.: Us extremism on telegram. Perspectives on Terrorism 15(2), 100–124 (2021) Khaund et al. 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[2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. 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IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Forbes: Pavel Durov — forbes.com. https://www.forbes.com/profile/pavel-durov/?sh=77a6811e14c5. [Accessed 26-10-2023] (2023) Ng and Loke [2020] Ng, L.H.X., Loke, J.Y.: Analyzing public opinion and misinformation in a covid-19 telegram group chat. IEEE Internet Computing 25(2), 84–91 (2020) Willaert et al. [2022] Willaert, T., Peeters, S., Seijbel, J., Van Raemdonck, N.: Disinformation networks: a quali-quantitative investigation of antagonistic dutch-speaking telegram channels. First Monday (2022) Sosa and Sharoff [2022] Sosa, J., Sharoff, S.: Multimodal pipeline for collection of misinformation data from telegram. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 1480–1489 (2022) Al-Rawi [2022] Al-Rawi, A.: News loopholing: Telegram news as portable alternative media. Journal of Computational Social Science 5(1), 949–968 (2022) Walther and McCoy [2021] Walther, S., McCoy, A.: Us extremism on telegram. Perspectives on Terrorism 15(2), 100–124 (2021) Khaund et al. [2020] Khaund, T., Hussain, M.N., Shaik, M., Agarwal, N.: Telegram: Data collection, opportunities and challenges. In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Loke, J.Y.: Analyzing public opinion and misinformation in a covid-19 telegram group chat. IEEE Internet Computing 25(2), 84–91 (2020) Willaert et al. [2022] Willaert, T., Peeters, S., Seijbel, J., Van Raemdonck, N.: Disinformation networks: a quali-quantitative investigation of antagonistic dutch-speaking telegram channels. First Monday (2022) Sosa and Sharoff [2022] Sosa, J., Sharoff, S.: Multimodal pipeline for collection of misinformation data from telegram. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 1480–1489 (2022) Al-Rawi [2022] Al-Rawi, A.: News loopholing: Telegram news as portable alternative media. Journal of Computational Social Science 5(1), 949–968 (2022) Walther and McCoy [2021] Walther, S., McCoy, A.: Us extremism on telegram. Perspectives on Terrorism 15(2), 100–124 (2021) Khaund et al. [2020] Khaund, T., Hussain, M.N., Shaik, M., Agarwal, N.: Telegram: Data collection, opportunities and challenges. In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Willaert, T., Peeters, S., Seijbel, J., Van Raemdonck, N.: Disinformation networks: a quali-quantitative investigation of antagonistic dutch-speaking telegram channels. First Monday (2022) Sosa and Sharoff [2022] Sosa, J., Sharoff, S.: Multimodal pipeline for collection of misinformation data from telegram. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 1480–1489 (2022) Al-Rawi [2022] Al-Rawi, A.: News loopholing: Telegram news as portable alternative media. Journal of Computational Social Science 5(1), 949–968 (2022) Walther and McCoy [2021] Walther, S., McCoy, A.: Us extremism on telegram. Perspectives on Terrorism 15(2), 100–124 (2021) Khaund et al. [2020] Khaund, T., Hussain, M.N., Shaik, M., Agarwal, N.: Telegram: Data collection, opportunities and challenges. In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Sosa, J., Sharoff, S.: Multimodal pipeline for collection of misinformation data from telegram. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 1480–1489 (2022) Al-Rawi [2022] Al-Rawi, A.: News loopholing: Telegram news as portable alternative media. Journal of Computational Social Science 5(1), 949–968 (2022) Walther and McCoy [2021] Walther, S., McCoy, A.: Us extremism on telegram. Perspectives on Terrorism 15(2), 100–124 (2021) Khaund et al. [2020] Khaund, T., Hussain, M.N., Shaik, M., Agarwal, N.: Telegram: Data collection, opportunities and challenges. In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Al-Rawi, A.: News loopholing: Telegram news as portable alternative media. Journal of Computational Social Science 5(1), 949–968 (2022) Walther and McCoy [2021] Walther, S., McCoy, A.: Us extremism on telegram. Perspectives on Terrorism 15(2), 100–124 (2021) Khaund et al. [2020] Khaund, T., Hussain, M.N., Shaik, M., Agarwal, N.: Telegram: Data collection, opportunities and challenges. In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Walther, S., McCoy, A.: Us extremism on telegram. Perspectives on Terrorism 15(2), 100–124 (2021) Khaund et al. [2020] Khaund, T., Hussain, M.N., Shaik, M., Agarwal, N.: Telegram: Data collection, opportunities and challenges. In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Khaund, T., Hussain, M.N., Shaik, M., Agarwal, N.: Telegram: Data collection, opportunities and challenges. In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). 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[2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Loke, J.Y.: Analyzing public opinion and misinformation in a covid-19 telegram group chat. IEEE Internet Computing 25(2), 84–91 (2020) Willaert et al. [2022] Willaert, T., Peeters, S., Seijbel, J., Van Raemdonck, N.: Disinformation networks: a quali-quantitative investigation of antagonistic dutch-speaking telegram channels. First Monday (2022) Sosa and Sharoff [2022] Sosa, J., Sharoff, S.: Multimodal pipeline for collection of misinformation data from telegram. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 1480–1489 (2022) Al-Rawi [2022] Al-Rawi, A.: News loopholing: Telegram news as portable alternative media. Journal of Computational Social Science 5(1), 949–968 (2022) Walther and McCoy [2021] Walther, S., McCoy, A.: Us extremism on telegram. Perspectives on Terrorism 15(2), 100–124 (2021) Khaund et al. [2020] Khaund, T., Hussain, M.N., Shaik, M., Agarwal, N.: Telegram: Data collection, opportunities and challenges. In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Willaert, T., Peeters, S., Seijbel, J., Van Raemdonck, N.: Disinformation networks: a quali-quantitative investigation of antagonistic dutch-speaking telegram channels. First Monday (2022) Sosa and Sharoff [2022] Sosa, J., Sharoff, S.: Multimodal pipeline for collection of misinformation data from telegram. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 1480–1489 (2022) Al-Rawi [2022] Al-Rawi, A.: News loopholing: Telegram news as portable alternative media. Journal of Computational Social Science 5(1), 949–968 (2022) Walther and McCoy [2021] Walther, S., McCoy, A.: Us extremism on telegram. Perspectives on Terrorism 15(2), 100–124 (2021) Khaund et al. [2020] Khaund, T., Hussain, M.N., Shaik, M., Agarwal, N.: Telegram: Data collection, opportunities and challenges. In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Sosa, J., Sharoff, S.: Multimodal pipeline for collection of misinformation data from telegram. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 1480–1489 (2022) Al-Rawi [2022] Al-Rawi, A.: News loopholing: Telegram news as portable alternative media. Journal of Computational Social Science 5(1), 949–968 (2022) Walther and McCoy [2021] Walther, S., McCoy, A.: Us extremism on telegram. Perspectives on Terrorism 15(2), 100–124 (2021) Khaund et al. [2020] Khaund, T., Hussain, M.N., Shaik, M., Agarwal, N.: Telegram: Data collection, opportunities and challenges. In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Al-Rawi, A.: News loopholing: Telegram news as portable alternative media. Journal of Computational Social Science 5(1), 949–968 (2022) Walther and McCoy [2021] Walther, S., McCoy, A.: Us extremism on telegram. Perspectives on Terrorism 15(2), 100–124 (2021) Khaund et al. [2020] Khaund, T., Hussain, M.N., Shaik, M., Agarwal, N.: Telegram: Data collection, opportunities and challenges. In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Walther, S., McCoy, A.: Us extremism on telegram. Perspectives on Terrorism 15(2), 100–124 (2021) Khaund et al. [2020] Khaund, T., Hussain, M.N., Shaik, M., Agarwal, N.: Telegram: Data collection, opportunities and challenges. In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Khaund, T., Hussain, M.N., Shaik, M., Agarwal, N.: Telegram: Data collection, opportunities and challenges. In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). 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[2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Willaert, T., Peeters, S., Seijbel, J., Van Raemdonck, N.: Disinformation networks: a quali-quantitative investigation of antagonistic dutch-speaking telegram channels. First Monday (2022) Sosa and Sharoff [2022] Sosa, J., Sharoff, S.: Multimodal pipeline for collection of misinformation data from telegram. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 1480–1489 (2022) Al-Rawi [2022] Al-Rawi, A.: News loopholing: Telegram news as portable alternative media. Journal of Computational Social Science 5(1), 949–968 (2022) Walther and McCoy [2021] Walther, S., McCoy, A.: Us extremism on telegram. Perspectives on Terrorism 15(2), 100–124 (2021) Khaund et al. [2020] Khaund, T., Hussain, M.N., Shaik, M., Agarwal, N.: Telegram: Data collection, opportunities and challenges. In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Sosa, J., Sharoff, S.: Multimodal pipeline for collection of misinformation data from telegram. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 1480–1489 (2022) Al-Rawi [2022] Al-Rawi, A.: News loopholing: Telegram news as portable alternative media. Journal of Computational Social Science 5(1), 949–968 (2022) Walther and McCoy [2021] Walther, S., McCoy, A.: Us extremism on telegram. Perspectives on Terrorism 15(2), 100–124 (2021) Khaund et al. [2020] Khaund, T., Hussain, M.N., Shaik, M., Agarwal, N.: Telegram: Data collection, opportunities and challenges. In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Al-Rawi, A.: News loopholing: Telegram news as portable alternative media. Journal of Computational Social Science 5(1), 949–968 (2022) Walther and McCoy [2021] Walther, S., McCoy, A.: Us extremism on telegram. Perspectives on Terrorism 15(2), 100–124 (2021) Khaund et al. [2020] Khaund, T., Hussain, M.N., Shaik, M., Agarwal, N.: Telegram: Data collection, opportunities and challenges. In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Walther, S., McCoy, A.: Us extremism on telegram. Perspectives on Terrorism 15(2), 100–124 (2021) Khaund et al. [2020] Khaund, T., Hussain, M.N., Shaik, M., Agarwal, N.: Telegram: Data collection, opportunities and challenges. In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Khaund, T., Hussain, M.N., Shaik, M., Agarwal, N.: Telegram: Data collection, opportunities and challenges. In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. 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Springer Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer
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In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Willaert, T., Peeters, S., Seijbel, J., Van Raemdonck, N.: Disinformation networks: a quali-quantitative investigation of antagonistic dutch-speaking telegram channels. First Monday (2022) Sosa and Sharoff [2022] Sosa, J., Sharoff, S.: Multimodal pipeline for collection of misinformation data from telegram. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 1480–1489 (2022) Al-Rawi [2022] Al-Rawi, A.: News loopholing: Telegram news as portable alternative media. Journal of Computational Social Science 5(1), 949–968 (2022) Walther and McCoy [2021] Walther, S., McCoy, A.: Us extremism on telegram. Perspectives on Terrorism 15(2), 100–124 (2021) Khaund et al. [2020] Khaund, T., Hussain, M.N., Shaik, M., Agarwal, N.: Telegram: Data collection, opportunities and challenges. In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Sosa, J., Sharoff, S.: Multimodal pipeline for collection of misinformation data from telegram. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 1480–1489 (2022) Al-Rawi [2022] Al-Rawi, A.: News loopholing: Telegram news as portable alternative media. Journal of Computational Social Science 5(1), 949–968 (2022) Walther and McCoy [2021] Walther, S., McCoy, A.: Us extremism on telegram. Perspectives on Terrorism 15(2), 100–124 (2021) Khaund et al. [2020] Khaund, T., Hussain, M.N., Shaik, M., Agarwal, N.: Telegram: Data collection, opportunities and challenges. In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Al-Rawi, A.: News loopholing: Telegram news as portable alternative media. Journal of Computational Social Science 5(1), 949–968 (2022) Walther and McCoy [2021] Walther, S., McCoy, A.: Us extremism on telegram. Perspectives on Terrorism 15(2), 100–124 (2021) Khaund et al. [2020] Khaund, T., Hussain, M.N., Shaik, M., Agarwal, N.: Telegram: Data collection, opportunities and challenges. In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Walther, S., McCoy, A.: Us extremism on telegram. Perspectives on Terrorism 15(2), 100–124 (2021) Khaund et al. [2020] Khaund, T., Hussain, M.N., Shaik, M., Agarwal, N.: Telegram: Data collection, opportunities and challenges. In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Khaund, T., Hussain, M.N., Shaik, M., Agarwal, N.: Telegram: Data collection, opportunities and challenges. In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. 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In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer
  7. Willaert, T., Peeters, S., Seijbel, J., Van Raemdonck, N.: Disinformation networks: a quali-quantitative investigation of antagonistic dutch-speaking telegram channels. First Monday (2022) Sosa and Sharoff [2022] Sosa, J., Sharoff, S.: Multimodal pipeline for collection of misinformation data from telegram. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 1480–1489 (2022) Al-Rawi [2022] Al-Rawi, A.: News loopholing: Telegram news as portable alternative media. Journal of Computational Social Science 5(1), 949–968 (2022) Walther and McCoy [2021] Walther, S., McCoy, A.: Us extremism on telegram. Perspectives on Terrorism 15(2), 100–124 (2021) Khaund et al. [2020] Khaund, T., Hussain, M.N., Shaik, M., Agarwal, N.: Telegram: Data collection, opportunities and challenges. In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. 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In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. 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[2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. 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In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Sosa, J., Sharoff, S.: Multimodal pipeline for collection of misinformation data from telegram. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 1480–1489 (2022) Al-Rawi [2022] Al-Rawi, A.: News loopholing: Telegram news as portable alternative media. Journal of Computational Social Science 5(1), 949–968 (2022) Walther and McCoy [2021] Walther, S., McCoy, A.: Us extremism on telegram. Perspectives on Terrorism 15(2), 100–124 (2021) Khaund et al. [2020] Khaund, T., Hussain, M.N., Shaik, M., Agarwal, N.: Telegram: Data collection, opportunities and challenges. In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Al-Rawi, A.: News loopholing: Telegram news as portable alternative media. Journal of Computational Social Science 5(1), 949–968 (2022) Walther and McCoy [2021] Walther, S., McCoy, A.: Us extremism on telegram. Perspectives on Terrorism 15(2), 100–124 (2021) Khaund et al. [2020] Khaund, T., Hussain, M.N., Shaik, M., Agarwal, N.: Telegram: Data collection, opportunities and challenges. In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Walther, S., McCoy, A.: Us extremism on telegram. Perspectives on Terrorism 15(2), 100–124 (2021) Khaund et al. [2020] Khaund, T., Hussain, M.N., Shaik, M., Agarwal, N.: Telegram: Data collection, opportunities and challenges. In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Khaund, T., Hussain, M.N., Shaik, M., Agarwal, N.: Telegram: Data collection, opportunities and challenges. In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer
  8. Sosa, J., Sharoff, S.: Multimodal pipeline for collection of misinformation data from telegram. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 1480–1489 (2022) Al-Rawi [2022] Al-Rawi, A.: News loopholing: Telegram news as portable alternative media. Journal of Computational Social Science 5(1), 949–968 (2022) Walther and McCoy [2021] Walther, S., McCoy, A.: Us extremism on telegram. Perspectives on Terrorism 15(2), 100–124 (2021) Khaund et al. [2020] Khaund, T., Hussain, M.N., Shaik, M., Agarwal, N.: Telegram: Data collection, opportunities and challenges. In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Al-Rawi, A.: News loopholing: Telegram news as portable alternative media. Journal of Computational Social Science 5(1), 949–968 (2022) Walther and McCoy [2021] Walther, S., McCoy, A.: Us extremism on telegram. Perspectives on Terrorism 15(2), 100–124 (2021) Khaund et al. [2020] Khaund, T., Hussain, M.N., Shaik, M., Agarwal, N.: Telegram: Data collection, opportunities and challenges. In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Walther, S., McCoy, A.: Us extremism on telegram. Perspectives on Terrorism 15(2), 100–124 (2021) Khaund et al. [2020] Khaund, T., Hussain, M.N., Shaik, M., Agarwal, N.: Telegram: Data collection, opportunities and challenges. In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Khaund, T., Hussain, M.N., Shaik, M., Agarwal, N.: Telegram: Data collection, opportunities and challenges. In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). 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[2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Khaund, T., Hussain, M.N., Shaik, M., Agarwal, N.: Telegram: Data collection, opportunities and challenges. In: Annual International Conference on Information Management and Big Data, pp. 513–526 (2020). Springer Weigand et al. [2022] Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. 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Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. 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Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. 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Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. 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Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. 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Springer Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. 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Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. 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[2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer
  12. Weigand, M., Weber, M., Gruber, J.: Conspiracy narratives in the protest movement against covid-19 restrictions in germany. a long-term content analysis of telegram chat groups. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+ CSS), pp. 52–58 (2022) Guhl and Davey [2020] Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. 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Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Guhl, J., Davey, J.: A safe space to hate: White supremacist mobilisation on telegram. Institute for Strategic Dialogue 26 (2020) La Morgia et al. [2021] La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. 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Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. 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Springer Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. 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Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer La Morgia, M., Mei, A., Mongardini, A.M., Wu, J.: Uncovering the dark side of telegram: Fakes, clones, scams, and conspiracy movements. arXiv preprint arXiv:2111.13530 (2021) Liu and Kim [2011] Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. 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[2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer
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IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. 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[2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Liu, B.F., Kim, S.: How organizations framed the 2009 h1n1 pandemic via social and traditional media: Implications for us health communicators. Public relations review 37(3), 233–244 (2011) Ng et al. [2020] Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. 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In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer
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In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, H.X.L., Lee, R.K.-W., Awal, M.R.: I miss you babe: Analyzing emotion dynamics during covid-19 pandemic. In: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pp. 41–49 (2020) Caliskan and Kilicaslan [2023] Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. 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Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. 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Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer
  17. Caliskan, C., Kilicaslan, A.: Varieties of corona news: a cross-national study on the foundations of online misinformation production during the covid-19 pandemic. Journal of Computational Social Science 6(1), 191–243 (2023) Bernard et al. [2021] Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Bernard, R., Bowsher, G., Sullivan, R., Gibson-Fall, F.: Disinformation and epidemics: anticipating the next phase of biowarfare. Health security 19(1), 3–12 (2021) Moffitt et al. [2021] Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. 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Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). 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Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. 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Springer Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. 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[2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. 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IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Moffitt, J., King, C., Carley, K.M.: Hunting conspiracy theories during the covid-19 pandemic. Social Media+ Society 7(3), 20563051211043212 (2021) Zheng [2013] Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Zheng, L.: Social media in chinese government: Drivers, challenges and capabilities. Government information quarterly 30(4), 369–376 (2013) Ng and Taeihagh [2021] Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. 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Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. 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[2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. 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Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. 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Springer Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. 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[2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H., Taeihagh, A.: How does fake news spread? understanding pathways of disinformation spread through apis. Policy & Internet 13(4), 560–585 (2021) Pal and Chua [2019] Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. 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Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. 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In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. 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[2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer
  22. Pal, A., Chua, A.Y.: Propagation pattern as a telltale sign of fake news on social media. In: 2019 5th International Conference on Information Management (ICIM), pp. 269–273 (2019). IEEE Liu and Wu [2018] Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer
  23. Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018) Hakak et al. [2020] Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. 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[2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer
  24. Hakak, S., Khan, W.Z., Bhattacharya, S., Reddy, G.T., Choo, K.-K.R.: Propagation of fake news on social media: challenges and opportunities. In: Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9, pp. 345–353 (2020). Springer Shao et al. [2017] Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Shao, C., Ciampaglia, G.L., Varol, O., Flammini, A., Menczer, F.: The spread of fake news by social bots. arXiv preprint arXiv:1707.07592 96, 104 (2017) Yuan et al. [2019] Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. 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Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). 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Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. 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Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. 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Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. 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Springer Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. 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Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. 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Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer
  26. Yuan, X., Schuchard, R.J., Crooks, A.T.: Examining emergent communities and social bots within the polarized online vaccination debate in twitter. Social media+ society 5(3), 2056305119865465 (2019) Cresci et al. [2017] Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer
  27. Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 963–972 (2017) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 686–697 (2023) Shao et al. [2018] Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. 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Springer Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. 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[2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer
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[2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nature communications 9(1), 1–9 (2018) Geeng et al. [2020] Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer
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[2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Geeng, C., Yee, S., Roesner, F.: Fake news on facebook and twitter: Investigating how people (don’t) investigate. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020) Wen et al. [2014] Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). 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Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. 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Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. 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Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. 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Springer Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. 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[2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Wen, S., Jiang, J., Xiang, Y., Yu, S., Zhou, W., Jia, W.: To shut them up or to clarify: Restraining the spread of rumors in online social networks. IEEE Transactions on Parallel and Distributed Systems 25(12), 3306–3316 (2014) Alieva et al. [2022] Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. 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Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. 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Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. 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[2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. 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[2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer
  32. Alieva, I., Ng, L.H.X., Carley, K.M.: Investigating the spread of russian disinformation about biolabs in ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775 (2022). IEEE Bragg et al. [2023] Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer
  33. Bragg, H., Jayanetti, H.R., Nelson, M.L., Weigle, M.C.: Less than 4% of archived instagram account pages for the disinformation dozen are replayable. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2023) Feng et al. [2022] Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer
  34. Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., Zheng, Q., Zhang, W., Lei, Z., Yang, S., et al.: Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems 35, 35254–35269 (2022) Heidari et al. [2021] Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer
  35. Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5 (2021). IEEE Chang et al. [2021] Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer
  36. Chang, H.-C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021) Himelein-Wachowiak et al. [2021] Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer
  37. Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H.A., Epstein, D.H., Leggio, L., Curtis, B.: Bots and misinformation spread on social media: Implications for covid-19. Journal of medical Internet research 23(5), 26933 (2021) Domashnev et al. [2019] Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Domashnev, P., Alexeev, V., Lavrukhina, T., Nazarkin, O.: Usage of telegram bots for message exchange in distributed computing. International Journal of Open Information Technologies 7(6), 67–72 (2019) de Oliveira et al. [2016] Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer
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IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer
  39. Oliveira, J.C., Santos, D.H., Neto, M.P.: Chatting with arduino platform through telegram bot. In: 2016 IEEE International Symposium on Consumer Electronics (ISCE), pp. 131–132 (2016). IEEE Idhom et al. [2018] Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer
  40. Idhom, M., Fauzi, A., Alit, R., Wahanani, H.E.: Implementation system telegram bot for monitoring linux server. In: International Conference on Science and Technology (ICST 2018), pp. 1089–1093 (2018). Atlantis Press Alrhmoun et al. [2023] Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer
  41. Alrhmoun, A., Winter, C., Kertész, J.: Automating terror: The role and impact of telegram bots in the islamic state’s online ecosystem. Terrorism and Political Violence, 1–16 (2023) Ng and Carley [2022] Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer
  42. Ng, L.H.X., Carley, K.M.: Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1), 3–19 (2022) Hallgren [2012] Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer
  43. Hallgren, K.A.: Computing inter-rater reliability for observational data: an overview and tutorial. Tutorials in quantitative methods for psychology 8(1), 23 (2012) Artstein and Poesio [2008] Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer
  44. Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Computational linguistics 34(4), 555–596 (2008) Grootendorst [2022] Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer
  45. Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022) Tausczik and Pennebaker [2010] Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer
  46. Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology 29(1), 24–54 (2010) Kacewicz et al. [2014] Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. 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[2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer
  47. Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology 33(2), 125–143 (2014) Newman [2005] Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer
  48. Newman, M.E.: A measure of betweenness centrality based on random walks. Social networks 27(1), 39–54 (2005) Ng and Carley [2023] Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. 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[2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer
  49. Ng, L.H.X., Carley, K.M.: A combined synchronization index for evaluating collective action social media. Applied network science 8(1), 1 (2023) Cai et al. [2023] Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer
  50. Cai, M., Luo, H., Meng, X., Cui, Y., Wang, W.: Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media. Information Processing & Management 60(2), 103197 (2023) Howard and Howard [2019] Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer
  51. Howard, J., Howard, J.: Bandwagon effect and authority bias. Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine, 21–56 (2019) Silvester [2021] Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer
  52. Silvester, C.: Authority bias. Decision Making in Emergency Medicine: Biases, Errors and Solutions, 41–46 (2021) Duffy et al. [2020] Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer
  53. Duffy, A., Tandoc, E., Ling, R.: Too good to be true, too good not to share: the social utility of fake news. Information, Communication & Society 23(13), 1965–1979 (2020) Gilani et al. [2017] Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer
  54. Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017) Samper-Escalante et al. [2021] Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer
  55. Samper-Escalante, L.D., Loyola-González, O., Monroy, R., Medina-Pérez, M.A.: Bot datasets on twitter: Analysis and challenges. Applied Sciences 11(9), 4105 (2021) Kloo and Carley [2023] Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer
  56. Kloo, I., Carley, K.M.: Social cybersecurity analysis of the telegram information environment during the 2022 invasion of ukraine. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 23–32 (2023). Springer
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